{ "127474-21859195": { "datetime_modified":"20241007T113035", "datetime_start":"20241009T160000", "datetime_end":"20241009T170000", "has_end_time":1, "date_start":"2024-10-09", "date_end":"2024-10-09", "time_start":"16:00:00", "time_end":"17:00:00", "time_zone":"America\/Detroit", "event_title":"Basic Research Computing on the Great Lakes Cluster Workshop", "occurrence_title":"", "combined_title":"Basic Research Computing on the Great Lakes Cluster Workshop: Jiashu Han", "event_subtitle":"Jiashu Han", "event_type":"Workshop \/ Seminar", "event_type_id":"21", "description":"\u201cGreat Lakes is a campus-wide high performance computing cluster (HPC) that serves the broad\r\nneeds of researchers across the university. The Great Lakes HPC Cluster is available to all researchers\r\non all campuses for simulation, modeling, machine learning, data science, genomics, and more. It\r\nuses Slurm workload manager to schedule jobs on a linux based operating system.\u201d\r\n(Source:\r\nhttps:\/\/its.umich.edu\/advanced-research-computing\/high-performance-computing\/great-lakes)\r\nThis workshop will cover some basic computing topics using the U-M Great Lakes Cluster.", "occurrence_notes":null, "guid":"127474-21859195@events.umich.edu", "permalink":"http:\/\/events.umich.edu\/event\/127474", "building_id":"1000166", "building_name":"East Hall", "campus_maps_id":"53", "room":"B737", "location_name":"East Hall", "has_livestream":0, "cost":"", "tags":["Mathematics"], "website":"", "sponsors":[ { "group_name":"Student AIM Seminar - Department of Mathematics", "group_id":"4902", "website":"" }, { "group_name":"Applied Interdisciplinary Mathematics (AIM) Seminar - Department of Mathematics", "group_id":"4882", "website":"" } ], "image_url":"", "styled_images":{ "event_thumb":"", "event_large":"", "event_large_2x":"", "event_large_lightbox":"", "group_thumb":"", "group_thumb_square":"", "group_large":"", "group_large_lightbox":"", "event_large_crop":"", "event_list":"", "event_list_2x":"", "event_grid":"", "event_grid_2x":"", "event_feature_large":"", "event_feature_thumb":"" }, "occurrence_count":1, "first_occurrence":21859195 } , "124296-21852857": { "datetime_modified":"20240815T095933", "datetime_start":"20241009T160000", "datetime_end":"20241009T170000", "has_end_time":1, "date_start":"2024-10-09", "date_end":"2024-10-09", "time_start":"16:00:00", "time_end":"17:00:00", "time_zone":"America\/Detroit", "event_title":"DCMB \/ CCMB Weekly Seminar", "occurrence_title":"", "combined_title":"DCMB \/ CCMB Weekly Seminar: Jingyi Jessica Li, PhD, \"Permutation Enhances the Rigor of Single-Cell Data Analysis\u201d", "event_subtitle":"Jingyi Jessica Li, PhD, \"Permutation Enhances the Rigor of Single-Cell Data Analysis\u201d", "event_type":"Lecture \/ Discussion", "event_type_id":"13", "description":"Abstract:\r\nEnsuring the reliability and accuracy of single-cell data analysis is critical, particularly in visualizing complex biological structures and addressing data sparsity. This talk introduces two novel statistical methods\u2014scDEED and mcRigor\u2014that leverage permutation-based techniques to enhance the rigor of these analyses.\r\n\r\nscDEED ([Xia et al., 2024, Nature Communications](https:\/\/www.nature.com\/articles\/s41467-024-45891-y)) addresses the challenge of evaluating the reliability of two-dimensional (2D) embeddings produced by visualization methods like t-SNE and UMAP, which are commonly used to visualize cell clusters. These methods, however, can sometimes misrepresent data structure, leading to erroneous interpretations. scDEED calculates a reliability score for each cell embedding, comparing the consistency between a cell's neighbors in the 2D embedding space and its pre-embedding neighbors. Cells with low reliability scores are flagged as dubious, while those with high scores are deemed trustworthy. Additionally, scDEED provides guidance for optimizing t-SNE and UMAP hyperparameters by minimizing the number of dubious embeddings, significantly improving visualization reliability across multiple datasets.\r\n\r\nmcRigor focuses on enhancing metacell partitioning in single-cell RNA-seq and ATAC-seq data analysis, a common strategy to address data sparsity by aggregating similar single cells into metacells. Existing algorithms often fail to verify metacell homogeneity, risking bias and spurious findings. mcRigor introduces a feature-correlation-based statistic to measure heterogeneity within a metacell, identifying dubious metacells composed of heterogeneous single cells. By optimizing metacell partitioning algorithm hyperparameters, mcRigor enhances the reliability of downstream analyses. Moreover, mcRigor allows for benchmarking and selecting the most suitable partitioning algorithm for a dataset, ensuring more robust discoveries.\r\n\r\nscDEED and mcRigor demonstrate the power of permutation-based approaches in refining single-cell data analysis, providing researchers with tools to achieve more accurate and reproducible insights into complex cellular processes.", "occurrence_notes":null, "guid":"124296-21852857@events.umich.edu", "permalink":"http:\/\/events.umich.edu\/event\/124296", "building_id":"1005047", "building_name":"Palmer Commons", "campus_maps_id":"188", "room":"Forum Hall", "location_name":"Palmer Commons", "has_livestream":0, "cost":"", "tags":["Lecture","Talk","Structural Biology","seminar","Science","Research","Public Health","Precision Health","Physics","Medicine","Mathematics","Life Science","Applications","Learning Health Systems","Human Genetics","Free","Engineering","Electrical Engineering and Computer Science","Discussion","Chemistry","Cardiovascular","Biosciences","Biomedical Engineering","Biology","Basic Science"], "website":"https:\/\/medschool.umich.edu\/events\/ccmb-seminar-series\/ccmb-seminar-jingyi-jessica-li-phd", "sponsors":[ { "group_name":"DCMB Seminar Series", "group_id":"3754", "website":"" } ], "image_url":"", "styled_images":{ "event_thumb":"", "event_large":"", "event_large_2x":"", "event_large_lightbox":"", "group_thumb":"", "group_thumb_square":"", "group_large":"", "group_large_lightbox":"", "event_large_crop":"", "event_list":"", "event_list_2x":"", "event_grid":"", "event_grid_2x":"", "event_feature_large":"", "event_feature_thumb":"" }, "occurrence_count":1, "first_occurrence":21852857 } }